llama: add Maple 20B-A1B ternary MoE architecture (CPU) (#27000)

* gguf-py: add Maple tensor constants

Add MODEL_ARCH.MAPLE, its "maple" name, and the tensor list for the
Maple 20B-A1B ternary MoE architecture: token embeddings, output,
attention with Q/K RMS norms, and per-expert FFN tensors.

* convert: add Maple HF->GGUF converter

Register MapleForCausalLM in the HF architecture map and add the
converter for the Maple 20B-A1B ternary MoE model: 24 layers, 256
experts with 8 active, sliding-window attention (SWA-512) interleaved
with global attention at a 3:1 ratio, partial rotary factor 0.5, and
per-expert weight stacking into merged 3D tensors.

* llama: add Maple architecture (20B-A1B ternary MoE)

Add the Maple 20B-A1B ternary MoE architecture: 24 layers, 256
experts with 8 active, sliding-window attention (SWA-512) interleaved
with global attention at a 3:1 ratio, and ternary TQ1_0/TQ2_0
quantization support.

- register LLM_ARCH_MAPLE between MAMBA2 and JAMBA
- implement llama_model_maple: Q/K RMS norms after projection (GEMMA4
  style), rope applied only on SWA layers (nope_on_global_attention),
  ISWA KV cache, and MoE FFN with swiglu gate clamp at +7 (DEEPSEEK4
  style)
- mark MAPLE as unsupported by the model saver (roundtrip skipped)

* tests: mark Maple as MoE-mandatory

Maple is always-MoE: the model throws when n_expert == 0, so the
test harness must only run the MoE config for LLM_ARCH_MAPLE.

* maple: apply review feedback (n_ff_exp_arr, get_arr, rope params)

- load_arch_hparams: use n_ff_exp_arr + n_ff_exp() accessor (upstream
  changed these from a scalar member during the rebase)
- sliding_window_pattern: get_arr, the pattern is mandatory for this arch
- partial_rotary_factor: read only from rope_parameters (base.py mirrors
  the top-level key automatically)
- document why TOKEN_EMBD/OUTPUT are forced to F16 (they are the two
  dense tensors in Maple, and the reference GGUFs ship them as F16)
- add @ModelBase.example("deepgrove/maple-preview")

* tests: add Maple to the SWA pattern array list

get_arr for maple.attention.sliding_window_pattern requires an array, but
the harness only emitted a per-layer array for the arches in its list, so
test-llama-archs -a maple failed to load the model.

Assisted-by: DeepSeek Harness

* maple: move swiglu_clamp_exp to the converter

The loader prefilled 7.0 and read the key optionally. The converter now
writes it and the loader reads it as required, because llama-graph.cpp
skips the clamp when the limit is 0 and an optional read would silently
run unclamped. The test harness provides the key for the same reason.

Also drops tensor_force_quant: base.py already forces FFN_GATE_INP to F32
and TOKEN_EMBD/OUTPUT to F16 for ternary file types.

Assisted-by: DeepSeek Harness

* convert: fix the LazyBase func signature in the Maple converter

ty flagged the stack() closure: it takes no argument, while LazyBase is
annotated with func: Callable[[Any], Any]. Pass the tensor list through
args instead of closing over it, the same way kimi_k3 does, so the
callable shape matches.

Assisted-by: DeepSeek Harness

gguf-py/gguf/constants.py

@@ -541,6 +541,7 @@ class MODEL_ARCH(IntEnum):
     ARWKV7           = auto()
     MAMBA            = auto()
     MAMBA2           = auto()
+    MAPLE            = auto()
     JAMBA            = auto()
     XVERSE           = auto()
     COMMAND_R        = auto()
@@ -1295,6 +1296,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.ARWKV7:           "arwkv7",
     MODEL_ARCH.MAMBA:            "mamba",
     MODEL_ARCH.MAMBA2:           "mamba2",
+    MODEL_ARCH.MAPLE:            "maple",
     MODEL_ARCH.JAMBA:            "jamba",
     MODEL_ARCH.XVERSE:           "xverse",
     MODEL_ARCH.COMMAND_R:        "command-r",
@@ -3487,6 +3489,23 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.SSM_NORM,
         MODEL_TENSOR.SSM_OUT,
     ],
+    MODEL_ARCH.MAPLE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+    ],
     MODEL_ARCH.JAMBA: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,